Early prediction of wheat yield using seasonal weather forecasts and the static stress drought index

Abstract

Abstract Crop yield prediction considering soil moisture as a proxy for water supply remains crucial for global food security. This study evaluates the potential of using seasonal weather forecasts combined with a drought index, Static Stress, based on both precipitation and soil moisture conditions to predict winter wheat yield 7 to 1 month in advance in Córdoba (South Spain). First, using observed climate and crop yield data we evaluate the use of Static Stress, as a potential crop yield predictor and compare it to a more traditionally used index, the SPEI, which is only based on precipitation conditions. Then we evaluate the performance of simple linear regression models to predict crop yields from forecasted Static Stress values calculated using weather forecast data from the ECMWF seasonal forecasting system (SEAS5). We find that Static Stress is better correlated to crop yield than SPEI and that Static Stress derived from seasonal forecasts has a good performance (R2 > 0.5; p-value < 0.05) for crop yield predictions of 4 or fewer months before harvest, i.e., from March to July. In this case study, these results indicate that drought indicators that consider soil moisture conditions are better predictors of crop yields than indicators that only consider precipitation. Furthermore, this study demonstrates the potential of using simple regression models together with mid-term forecasts of the Static Stress index to maximize cereal yields and mitigate drought impacts.